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Procedure for unmasking localization information from ProstaScint scans for prostate radiation therapy treatment planning
Int. J. Radiation Oncology Biol. Phys., Vol. 60, No. 2, pp. 654 – 662, 2004 Copyright © 2004 Elsevier Inc. Printed in the USA. All rights reserved 0360-3016/04/$–see front matter
doi:10.1016/j.ijrobp.2004.05.034
PHYSICS CONTRIBUTION
PROCEDURE FOR UNMASKING LOCALIZATION INFORMATION FROM PROSTASCINT SCANS FOR PROSTATE RADIATION THERAPY TREATMENT PLANNING J. KEITH DEWYNGAERT, PH.D.,* MARILYN E. NOZ, PH.D,† BRUCE ELLERIN, M.D.,储 ELISSA L. KRAMER, M.D., GERALD Q. MAGUIRE, JR., PH.D.,‡ AND MICHAEL P. ZELEZNIK, PH.D.§ *Department of Radiation Oncology, New York University Medical Center, New York, NY; †Department of Radiology, New York University Medical Center, New York, NY; ‡Institution for Microelectronics and Information Technology, Royal Institute of Technology, Kista, Sweden; §Department of Radiation Oncology, University of Utah, Salt Lake City, UT; and 储RAHD Oncology Products, St. Louis, MO Purpose: To demonstrate a method to extract the meaningful biologic information from 111In-radiolabeled capromab pendetide (ProstaScint) SPECT scans for use in radiation therapy treatment planning by removing that component of the 111In SPECT images associated with normal structures. Methods and Materials: We examined 20 of more than 80 patients who underwent simultaneous 99mTc/111In SPECT scans, which were subsequently registered to the corresponding CT/MRI scans.A thresholding algorithm was used to identify 99mTc uptake associated with blood vessels and CT electron density associated with bone marrow. Corresponding voxels were removed from the 111In image set. Results: No single threshold value was found to be associated with the 99mTc uptake that corresponded to the blood vessels. Intensity values were normalized to a global maximum and, as such, were dependent upon the quantity of 99mTc pooled in the bladder. The reduced ProstaScint volume sets were segmented by use of a thresholding feature of the planning system and superimposed on the CT/MRI scans. Conclusions: ProstaScint images are now closer to becoming a biologically and therapeutically useful and accurate image set. After known sources of normal intensity are stripped away, the remaining areas that demonstrate uptake may be segmented and superimposed on the treatment-planning CT/MRI volume. © 2004 Elsevier Inc. ProstaScint, Volume fusion, Volume subtraction, Prostate cancer treatment planning, Radioimmunoguided therapy.
traditional external-beam therapy with respect to sparing of normal tissues, all techniques still suffer from nonspecificity of dose application. The target volume for both techniques is the anatomic prostate, whereas the true biologic clinical target volume is the prostate cancer, which may occupy a smaller or a larger physical volume than the anatomic prostate. Because of the direct adjacency of dose-sensitive critical structures (the bladder superiorly, the rectum posteriorly, the penile bulb anteroinferiorly, and the urethra interiorly), the use of the anatomic prostate (rather than the prostate cancer) as the basis for the clinical target volume necessarily limits the total dose that can be administered to the tumor insofar as the adjacent structures, rather than the prostate cancer, dictate the doses that are employed, even when the prostate cancer lies at a relatively safe distance
INTRODUCTION Because radiation therapy dose escalation may be associated with a higher cure rate for certain localized cancers, such as prostate cancer (1–5), and although dose escalation, in principle, is associated with an increased risk of complications to surrounding normal tissues that can also be affected by factors of technique and irradiated volume (6 – 8), interest has always been great among radiation oncologists in finding ways to confine escalated dose to tumor while sparing surrounding normal tissues. In the case of localized prostate cancer, techniques currently in use include interstitial brachytherapy (9 –11), three-dimensional (3D) conformal radiation therapy (12), and intensity-modulated external-beam radiation therapy (IMRT) (13–15). Although these techniques represent considerable improvements over
LA, October, 2002. Acknowledgment—We thank Antoinette Murphy-Walcott, CNMT and Chris J. Shettino, B.A. for fusing the images. Received Oct 22, 2003 and in revised form May 10, 2004. Accepted for publication May 12, 2004.
Reprint requests to: J. Keith DeWyngaert, Ph.D., New York University, 550 First Avenue, New York, NY 10016. Tel: (212) 2635836; Fax: (212) 263-6274; E-mail: keith.dewyngaert@ med.nyu.edu Presented at the annual meeting of the American Society for Therapeutic Radiology and Oncology (ASTRO), New Orleans, 654
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from these surrounding normal tissues when appropriate contemporary conformal techniques are used. More recent radiobiological thinking has come to favor the application of higher fractional doses to the histologic prostate cancer as the tumor burden within the prostate increases (16). Because of the well-established positive correlation between increasing total dose and acute effects on the one hand and between increasing fraction size and late effects in normal tissues on the other hand, (17) tumorspecific targeting for the purpose of either fraction-size escalation (to 3 Gy/fraction or higher)(13) or of total-dose escalation (to 81 Gy or higher) (18) needs to be performed with utmost precision to minimize acute and late effects on surrounding normal tissues. Fortunately, progress in directing dose to targeted structures with increasingly conformal treatment techniques has been paralleled by progress in defining actual tumor extent with sophisticated molecular and radiographic techniques. Molecular markers such as glycoprotein A-80 (19) and osteocalcin (20) are often found selectively in prostate cancer cells and can be used to distinguish malignant from benign histology within a single specimen. Capromab pendetide is a monoclonal antibody that adheres to prostate-specific membrane antigen (PSMA) and, when combined with 111In, the radiolabeled capromab pendetide (ProstaScint; Cytogen Corporation, Princeton, NJ), can be used to image prostate cancer (21), primarily in lymph nodes and distant soft-tissue sites (22). At the same time, specialized imaging protocols have been developed (e.g., in sonography) to select for physical characteristics (23) that may be expressed more commonly in diseased than in healthy tissue (24), in malignant than in benign tissue (25), and in cancerous than in normal prostate tissue (26). A similar concept has been applied in nuclear magnetic resonance imaging and magnetic resonance spectroscopy to distinguish prostate cancer from benign tissue (27), which has also found application in radiation treatment planning (28, 29) and treatment evaluation (30). A further refinement of this process, which aims to increase the specificity of image-based detection of tumors, is the use of fusion technology to link the image sets of two distinct imaging modalities, such as CT and MRI, for radiosurgery planning (31) and magnetic resonance spectroscopy and CT and/or ultrasound for prostate cancer (32). Since the introduction of digital imaging in radiology, images have been transformed from mere records of radiation passage through photographic emulsion to dynamic data sets whose numerical correlates can be manipulated to emphasize volumes of interest and to efface radiographic noise. One of the earliest applications of this principle was digital subtraction angiography, but the principle has subsequently been applied to magnetic resonance angiography whereby unwanted information is digitally eliminated from the data set to emphasize the volume of interest (33). As the number of molecular techniques and imaging protocols for prostate cancer detection increases and becomes more accurate, the possibility of combining techniques and protocols to in-
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crease the accuracy of prostate cancer identification for radiation treatment planning presents itself. In a preliminary experiment, a previously described and validated fusion technique (34) was used to retrospectively match (fuse) the ProstaScint SPECT scans of more than 80 patients with CT scans, MRI scans, or both. With the addition of the anatomic information, the fusion process was shown to improve the diagnostic interpretation of the SPECT data (35, 36). In this study, we examine whether the imaging information obtainable from MRI and/or CT in combination with ProstaScint, when processed by the techniques of image fusion and digital subtraction, might increase the specificity of prostate cancer identification of postprostatectomy patients with a rising prostate-specific antigen (PSA) to a degree that would allow clinicians to reliably distinguish between those patients in whom recurrent prostate cancer is confined to the prostate bed and those in whom the prostate cancer has spread outside the prostate bed. If cancer is confined to the prostate (in preprostatectomy or postprostatectomy patients), then radiation therapy in the form of external therapy, or brachytherapy, is the treatment of choice. If cancer has spread outside the prostate, then systemic therapy is generally applied. This article is an initial step toward using routine diagnostic imaging information to achieve the goal of localizing cancer for treatment planning. It follows on the work that has been done for better placement of seed implants in brachytherapy (29). METHODS AND MATERIALS Patients A group of 20 patients was selected for analysis from a larger population of patients accrued since 1998 who had fused volume data sets consisting of ProstaScint scans and either MRI (n ⫽ 9) or CT (n ⫽ 11) studies. These 20 patients were selected because they had been referred for treatment to our Radiation Oncology Department. All patients except 1 were post–radical prostatectomy. The patients had been referred for ProstaScint scans because of rising PSA levels. All patient data were acquired by use of routine protocols; no special acquisition procedures were performed. The patients ranged in age from 47 to 81. This study was a retrospective study approved by the Institutional Board of Research Associates. SPECT volumes All patients underwent planar and SPECT scans of the chest, abdomen, and pelvis 96 hours (4 days) after infusion of 185–222 MBq (5– 6 mCi) of 111In-labeled ProstaScint. On the day the ProstaScint scans were to be acquired, the patients were injected with 222 MBq (6 mCi) of 99mTc red blood cells (RBCs) so that a simultaneous blood pool scan could be acquired. The ProstaScint and RBC images were obtained concurrently and were spatially aligned as confirmed by quality control measurements. All data sets were acquired on a dual-headed gamma camera (GC7200-DI;
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Toshiba America Medical Systems, Tustin, CA) that was fitted with medium-energy collimators and used 20% energy windows centered on 173 keV and 247 keV to acquire the 111In images and used a 15% energy window centered on 140 keV to acquire the 99mTc blood pool images. Volume data sets were acquired into 128 ⫻ 128 ⫻ 2 byte matrices with a total of 90 projections and a time per projection of 50 seconds and were reconstructed by application of a commercially supplied algorithm that consisted of a Butterworth prefilter (eighth order; cutoff frequency ⫽ 0.18 cycles/cm) and ramp backprojection, which resulted in a cubic voxel with dimensions of 4.3 mm on a side. The gamma camera full width at half maximum (FWHM) is 9.3 mm. MRI Volumes All the MRI volumes were acquired on a 1.5 T scanner (Siemens Magneton Symphony or Vision System, Iselin, NJ) by implementation of the standard pelvic anatomic protocol, which resulted in several different T1-weighted and T2-weighted volume data sets. The volume data sets used for the fusion were reconstructed into matrix sizes of 512 ⫻ 512 ⫻x 2 bytes with an x-y pixel size that ranged from 0.49 to 0.53 mm and a total spacing between slices that ranged from 4.4 to 5.3 mm with one scan at 8.8 mm. The axial T2 Spin Echo sequence was used for the fusion. CT Imaging All the CT volumes were acquired (Highlight Advantage or High Resolution, General Electric Corporation, Milwaukee, WI) after the administration of oral and/or i.v. contrast (Conray 43; Mallinkrodt, St. Louis, MO). The resulting 50 to 60 transaxial sections were each acquired into a 512 ⫻ 512 ⫻ 2 byte matrix. All the volume sets had a slice thickness that ranged from 5.0 to 7.0 mm with one image set at 8.8 mm. The x-y pixel size varied between 0.64 and 0.94 mm. Data handling All volume data sets (CT/MRI/SPECT) were transferred via the hospital ethernet (TCP/IP) network to a common computer system (Sun SPARC Ultra10; Sun Microsystems Inc., Mountain View, CA) and converted to a common, standard image format (Interfile/AAPM)(37). Original data were transferred in DICOM 3 (CT/MRI) or NEMA 1 (SPECT) format and were not altered by the conversion to Interfile/AAPM format. Software modules were developed to convert the processed SPECT scans, which had been aligned with the diagnostic CT/MRI scan to DICOM 3 format, so that they could be imported by the treatmentplanning system (Eclipse; Varian Medical Systems, Palo Alto, CA). These warped SPECT studies were disguised as MRI scans because there was no SPECT modality assignment available in the planning system. Any modality specification other than “MR” defaulted in our treatment-planning system to a CT, and this assignment restricted the intensity levels to expected Hounsfield numbers. If we des-
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ignated that the SPECT volumes were of modality “MR,” the treatment-planning system accepted them without perversion of the intensity values.
Volume fusion Image fusion has previously been attempted for ProstaScint SPECT scans by application of a vascular outlining method, in which large vessels of the abdomen were outlined by computer-generated wire rings on SPECT 99mTc blood-pool images and CT images. The vascular outlines on the two-image sets were then aligned manually, and the resulting spatial relationships served as the basis for the registration (38). In our study, fusion of ProstaScint SPECT and CT/MRI scans were performed by a computer program, codeveloped with RAHD Oncology Products, that performs landmark-based fusion of 2 volumes by application of a polynomial warping algorithm (34). The registration algorithm incorporated in this tool has previously been extensively validated (39, 40) and can be used to produce an affine (the original relationship between the structures involved, that is, the straightness of lines, parallelism, and the ratio between the length of 2 segments of the same line is preserved) or nonaffine transformation, in this case, warping (nonaffine transformation has more degrees of freedom, which allows line lengths and angles to change, allows straight lines to become curved, and does not preserve parallelism). The technical approach to image fusion in the thorax, abdomen, and pelvis is more complicated than in the brain because different shapes, sizes, and angles can occur; bowel can distend and deflate; and respiratory motion is always present. Therefore, the ability to stretch, warp, or deform images is important to achieving a good fusion. Even when images for fusion are acquired prospectively, differences in positioning may be reduced. However they are not completely eliminated (41). Arbitrarily chosen slices (with optionally superimposed isolines of the user’s choosing) are presented together in all 3 planes (axial, coronal, and sagittal) for both the anatomic and functional volume sets (6 views) or as larger views (2) in 1 user-selected plane (42). Corresponding point pairs (landmarks) were chosen on concurrently viewed slices that display the same physiologic point or structure. Landmarks could be selected in multiple planes simultaneously. When a landmark was chosen, the corresponding volume-element in the image, the voxel, was marked, a sequential number generated, and all 3 planes triangulated to this point. All landmarks were recorded at the respective 3D point in distance units (mm in this case). The 3D paired landmarks were used to generate the transformed volume. The eigenvalues of the matrix of coefficients are generated from the 3D paired landmarks by employing a weighted least-squares linear regression followed by a Gauss-Jordan matrix inversion. This procedure limited the effects of mismatched landmarks and provided an easy way to generate transformation coefficients for arbitrary volume data sets. Finally, polynomial equations
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Fig. 1. 99mTc images were registered to the computed tomography (CT) images with landmarks indicated on orthogonal images. 99mTc intensities above the threshold limit were overlaid on the CT images and varied to determine the appropriate threshold value. The corresponding isoline values are shown as an overlay on the 99mTc images. (Top) SPECT with isolines delineating regions of intensity above the threshold. (Bottom) CT with superimposed regions of SPECT intensity above threshold. (A) Coronal view; (B) axial view.
formed from the eigenvalues, together with resampling, determined the voxel values in the warped volume that brought it into correspondence with the reference volume; that is, each original data point was moved to the transformed position. The transformation could be performed by use of only the first-order terms (12 parameters in all) or by use of the second-order terms (30 parameters in all) from the polynomial equations (34, 42). A completely manual (translate, rotate, and scale only) affine transformation was also available to do a complete fusion or to “tweak” the above transformation to bring the 2 volumes into better alignment if required. In this experiment, the SPECT volume was always transformed to correspond to the anatomic volume; that is, the SPECT data sets were warped (preserving total counts) to fit the MRI or CT. Hence the original anatomic data sets that would be used in the treatment-planning process were not altered. Magnetic resonance volumes may contain geometrical distortions. However, the purpose of registration between SPECT and MR is to assign anatomic meaning to the SPECT information, not geometrical meaning. For purposes of treatment planning, a CT scan is used as a geometrically accurate reference image.
Thresholding Once the new (warped) volume had been produced from the original, untransformed volume data set, it can be resliced and evaluated side-by-side or merged with the reference slices. In side-by-side mode, isointensity lines (isolines) from the warped volume could be superimposed on the reference slices. The SPECT radionuclidic uptake was normalized to that studies’ maximum counts. As the 99m Tc was tagged to red blood cells, the 99mTc images represented blood vessels and normal structure reservoirs. The process was to select through visual inspection the intensity value (isoline) that most closely matched the blood vessels seen on the reference (CT/MRI) image set. As the isolines were varied, the reference image set was examined by paging through the reference set of axial, coronal, and sagittal images and observing the overlap of the 99mTc isolines. An alternate presentation of the same process, shown in Fig. 1, could be achieved by blending the 2 image sets, CT and 99mTc, in an overlay mode such that only those voxels of the 99mTc image whose intensities exceeded the selected isoline (threshold) value were viewable. The advantage of this representation was that it maintained the intensity variation of the SPECT image in the overlay. The images could
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Fig. 2. The 99mTc threshold values representing the best fit of the computed tomography or magnetic resonance imaging blood vessels are shown for the 20 cases studied.
be viewed in the 3 orthogonal planes simultaneously with varied levels of transparency. After the isoline or threshold value had been established, a new reduced 99mTc image set was created that contained only those voxels whose counts exceeded the threshold value. Because the SPECT images were acquired as a simultaneous dual-SPECT image set, the unwarped 99mTc is self-registered with the original ProstaScint 111In volume set. Therefore, the new thresholded 99mTc image set, which consisted of the voxels identified as representative of bloodpooling structures could be directly subtracted from the 111 In ProstaScint image set. In practice, the voxels were not subtracted, but rather assigned an intensity value of zero. Boolean Subtraction of bone marrow ProstaScint uptake associated with the bone marrow was removed through the introduction of Boolean operations available within the treatment-planning system. Contours were generated from the ProstaScint pixel values by application of the planning system’s threshold segmentation algorithm. The same warping transformation described earlier for the 99mTc images was utilized to register the ProstaScint images to the reference image set, CT or MRI. As before, the SPECT images were superimposed on the reference images to allow inspection of threshold levels for anatomic relevance. A threshold value was chosen that excluded the background 111In counts in the ProstaScint SPECT scans. The same threshold segmentation algorithm was also applied to the CT data set to define the pelvic bony structures.
Areas of overlap between the CT-defined bone and the 111In scan were excluded as a normal pooling site for the ProstaScint. This action was accomplished by use of a Boolean operator that subtracted the bone volume from the ProstaScint segmented volume. RESULTS The threshold values chosen for the 20 patients are shown in Fig. 2. The counts per pixel were normalized to the maximum counts for the volume set. For the 99mTc data sets, the highest counts were associated with the bladder. The threshold values range from 9% to 54% of the maximum volume intensity. All 99mTc studies were successfully thresholded and subtracted from the ProstaScint study. Figure 3 depicts the thresholded 99mTc study, overlay of the isoline value onto the 111In image, and the subsequent 111In after the overlapping 99mTc image voxels were zeroed. The SPECT volume studies, which included the subtracted ProstaScint study, were fused with the reference image set, and the resulting SPECT volumes were saved to disk. These warped SPECT studies were reformatted into DICOM and imported into the treatment-planning system. The SPECT scans were successfully segmented using the treatment-planning software’s thresholding capabilities. This thresholding algorithm was applied to segment bone on CT and sites of 111In activity on the ProstaScint images, which created 3D volume sets of bone and ProstaScint.
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Fig. 3. The process of removing normal structures from the 111In volume set starts with identifying the threshold value for 99mTc images as shown in Fig. 1. A new thresholded 99mTc image set was created (A) and superimposed onto the 111 In images (B) and subsequently these voxels were set to zero intensity in the 111In images (C). This last set was sent to the treatment-planning system for evaluation.
Figure 4 shows the contours of the pelvic bone, the 99mTc blood uptake, and the remaining 111In uptake superimposed onto either a CT image (Fig. 4A) or a ProstaScint image (Fig. 4B). The final step of removing potential background uptake from normal tissues was to subtract the segmented bone from the segmented ProstaScint. All the volume sets were warped before exporting to the treatment-planning system, so no additional registration was required. DISCUSSION Molecular imaging is increasingly being considered as a method for target localization in radioimmunoguided radiation therapy (43). ProstaScint is an example of a marker developed for use in targeting prostate cancer through specificity for PSMA (32, 44, 45). However, the ProstaScint SPECT images are difficult to interpret because the tagged antibody or disassociated 111In collects in structures such as the vascular structures, liver, kidneys, bone, and bone marrow (see Fig. 3B). Our goal was to establish a clinically applicable procedure for taking these seemingly unintelli-
gible images, by radiation oncology standards, and transforming them such that they can be a useful component in the treatment-planning process. This transformation involved filtering out the background of information that obscured the pathologic findings, warping the image set to match a reference set, importing the images into our commercial treatment-planning system, and segmenting the SPECT images. We have not yet evaluated how our method would help the radiation oncologist with the actual treatment plan. We are refining our subtraction technique, and, after that, we will conduct a retrospective study to assess what, if any, impact our method would have on the actual treatment plans. The subtraction process initiates with a determination of the threshold value that corresponds to the blood vessels. As such, it builds upon the warping algorithm and the selfregistration of the dual-SPECT imaging. The threshold value that most closely resembles the underlying biology is dependent upon both scanning and patient parameters, such as the total scan volume, the image contrast and spatial
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resolution, and bladder filling (46). Phantom tests designed to determine threshold values for use in SPECT imaging have resulted in a wide range of values from 40% for good-contrast images to 70% for low-contrast images (46). We have, in general, found that much lower thresholds have applied when considering the abdomen and pelvis as the region of interest. No single formula can be applied to determine the appropriate threshold when considering such a large volume that contains many sites of radionuclidic uptake. For example, in defining threshold values that represent vascular structures for 99mTc, our values were dominated by the bladder intensity and ranged from 9% to 54%. This large variation in maximum bladder intensity occurs for two reasons: (1) physiologic parameters associated with the patient, such as whether or not the patient is taking diuretics, and (2) the ability of the patient to void completely and how long before the acquisition of the pelvic SPECT scan the patient has attempted to empty his bladder. This problem can perhaps be overcome by the use of a Foley catheter. We have not applied this method because of the discomfort and inconvenience it would cause to the patient. Instead, we have pursued a mathematical solution that suppresses the bladder activity by use of an averaging algorithm within an ellipsoid volume encompassing the bladder. Outside of this ellipsoidal volume, the blood-vessel activity as determined by the 99mTc scans is subtracted from the 111In after normalization of the data sets on the basis of peak activity within the descending aorta. The reason for this attention to suppression of the bladder uptake may be seen from Fig. 4, which illustrates the capabilites of this approach to localize suspicious uptake in regions in close proximity to the bladder. Figure 1 demonstrates how overwhelming this uptake within the bladder may be compared with the other sites within the SPECT image. Care must be taken not to remove activity from the 111In scan that might be indicative of prostate disease or prostate bed recurrence. A patient with an intact prostate was chosen for Fig. 4 to help illustrate the concepts of localizing disease by remov-
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ing sources of activity from the bone marrow and blood vessels. Enough significant uptake occurs within the prostate to segment on the treatment-planning system. The process was the same for postprostatectomy patients. The registration/fusion tool used here requires between 10 to 20 minutes for the registration to be completed; that is, volumes reviewed, landmarks chosen, warp performed, and result accepted. The actual warping transformation itself takes 10 to 50 seconds and depends only on the number of voxels to be transformed. Additionally, this tool is user friendly and can be used by trained individuals who have a fair knowledge of anatomy (e.g., a radiologic technologist, medical student, or physician). This tool has proved to be suitable for other routine clinical use, particularly in terms of speed and user friendliness. A convenient feature of this tool is that by allowing the selection of landmarks in multiple planes, the user can view the physiologically/anatomically corresponding points from different points of view, which often strengthens the positive identification of corresponding features. Furthermore, the incorporation of isolines into the tool adds additional information and can be very useful on the actual display. The ability to merge both the 2D slices and 3D volume sets to visually demonstrate the degree of overlap is a very useful complement to the isoline display. Although the method is interactive, in that point pairs must be chosen, a recent study has shown that the chief objections to interactive versus automated registration (specifically interobserver and intraobserver differences) are not as severe as they were once thought to be (47). Methods that are claimed to be fully automatic generally require user interaction either before or during the registration process (48 –50) or require a tremendous amount of prospective preparation, which, thus, excludes retrospective correlation (41), especially when the region to be matched is not the brain (51). Treatment-planning systems are not designed to detect subtle specificity differences of SPECT uptake above the
Fig. 4. Contours are presented for the computed tomography (CT)-derived pelvic bone anatomy (green), the 99mTc blood vessels (yellow) and the unaltered 111In ProstaScint uptake (cyan). The 3 contours are overlaid on a (A) CT image and on the (B) corresponding ProstaScint image. Areas of overlap are shown representing the blood vessels as well as a ProstaScint-only uptake located in the prostate as seen on the CT image.
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background and noise. They are designed to work with a defined set of well-described volumes. Nuclear medicine systems are designed to guide the user in defining areas of enhanced radionuclidic activity but are not designed, in general, for the segmentation of volumes, as might be necessary in radiation therapy treatment planning. We extended the ability of our Nuclear medicine– based system to allow for the subtraction of areas contained within a given threshold value (isoline) from an accompanying registered image. If the CT bone-density threshold values were applied to the 111In scans in our Nuclear medicine system, the area where the bones were (even though not usually visible unless some disassociated 111In is present in the scan) would be subtracted but not the areas enclosed by the bones (i.e., the bone marrow), which was visible on the SPECT scan. Outlining the bone marrow only on either the CT or the SPECT was very difficult. Because the treatment-planning system segments everything within the contours by outlining the bone by use of a thresholding technique, the bone marrow was included as an internal component of the contour. This ability to perform Boolean operations between different segmented volume sets is a powerful tool that allows molecular imaging to be integrated more fully into
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the treatment-planning process by removing uptake associated with normal structures in an automated approach. In conclusion, the molecular images when fused with the CT/MRI and integrated into the treatment-planning workstation, may identify areas either within the prostate or external to the prostate that are suggestive of disease. We have demonstrated the proof of concept of incorporating the ProstaScint data for use in external-beam radiation therapy treatment planning in a meaningful way. Nuclear medicine data may be integrated into the radiation oncology treatment-planning environment through the application of thresholding algorithms that segment the SPECT images for structure. The background counts associated with normal structures may be removed from the data set, which allows the oncologist to see through the fog associated with the ProstaScint image set by focusing on uptake not associated with normal structures. In this regard, Boolean operators are powerful tools to help decode the SPECT image pattern. Inclusion of this additional information may help redefine the target for both conventional 3D and IMRT treatment planning, as has been demonstrated for brachytherapy treatment of prostatic disease (29, 42, 43).
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noma using indium-111 capromab pendetide. Cancer 1998;83:739 –747. Haseman MK, Rosenthal SA, Polascik TJ. Capromab pendetide imaging of prostate cancer. Cancer Biother Radiopharm 2000;15:131–140. Madsen EL, Dong F, Frank GR, et al. Interlaboratory comparison of ultrasonic backscatter, attenuation, and speed measurements. J Ultrasound Med 1999;18:615– 631. Noritomi T, Sigel B, Swami V, et al. Carotid plaque typing by multiple-parameter ultrasonic tissue characterization. Ultrasound Med Biol 1997;23:643– 650. Tateishi T, Machi J, Feleppa EJ, et al. In vitro B-mode ultrasonographic criteria for diagnosing axillary lymph node metastasis of breast cancer. J Ultrasound Med 1999;18:349 – 356. Balaji KC, Fair WR, Feleppa EJ, et al. Role of advanced 2 and 3-dimensional ultrasound for detecting prostate cancer. J Urol 2002;168:2422–2425. Kim HW, Buckley DL, Peterson DM, et al. In vivo prostate magnetic resonance imaging and magnetic resonance spectroscopy at 3 Tesla using a transceive pelvic phased array coil: Preliminary results. Invest Radiol 2003;38:443– 451. Parker CC, Damyanovich A, Haycocks T, et al. Magnetic resonance imaging in the radiation treatment planning of localized prostate cancer using intra-prostatic fiducial markers for computed tomography co-registration. Radiother Oncol 2003;66:217–224. Ellis RJ, Regan F, Lin K, et al. Radioimmunoguided imaging of prostate cancer foci with histopathological correlation. Int J Radiat Oncol Biol Phys 2001;49:1281–1286. Pickett B, Kurhanewicz J, Fein B, et al. Use of magnetic resonance imaging and spectroscopy in the evaluation of external beam radiation therapy for prostate cancer [Abstract]. Int J Radiat Oncol Biol Phys 2003;57(Suppl):S163–S164. Kooy HM, van Herk M, Barnes PD, et al. Image fusion for stereotactic radiotherapy and radiosurgery treatment planning. Int J Radiat Oncol Biol Phys 1994;28:1229 –1234. Mizowaki T, Cohen GN, Fung AY, Zaider M. Towards integrating functional imaging in the treatment of prostate cancer with radiation: The registration of the MR spectroscopy imaging to ultrasound/CT images and its implementation in treatment planning. Int J Radiat Oncol Biol Phys 2002;54: 1558 –1564. Kreitner KF, Kalden P, Neufang A, et al. Diabetes and peripheral arterial occlusive disease: Prospective comparison of contrast-enhanced three-dimensional MR angiography with conventional digital subtraction angiography. AJR 2000;174: 171–179. Gorniak RJT, Kramer EL, Maguire Jr GQ, et al. Evaluation of a semi-automatic 3D fusion technique applied to molecular imaging and MRI brain/frame volume data sets. J Med Syst 2003;27:141–156. Schettino CJ, Noz ME, Kramer EL, et al. Impact of Image Fusion of the 111In capromab pendetide with MR or CT in patients with recurrent prostate cancer [Abstract]. J Nucl Med 2001;42(Suppl):294. Schettino CJ, Noz ME, Taneja S, et al. impact of fusion of
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In-111 capromab pendetide volume data sets with those from MRI or CT in patients with recurrent prostate cancer. AJR Am J Roentgenol 2004;183:519 –524. Reddy DP, Maguire Jr GQ, Noz ME, et al. Automating image format conversion—twelve years and twenty-five formats later. In: Lemke HU, Inamura K, Jaffee CC, Felix R, Computer assisted radiology—CAR’93. Berlin: Springer-Verlag; 1993. p. 253–258. Hamilton RJ, Blend MJ, Pelizzari CA, et al. Using vascular structure for CT-SPECT registration in the pelvis. J Nucl Med 1999;40:347–351. Maguire Jr GQ, Noz ME, Rusinek H, et al. Graphics applied to image registration. IEEE Comput Graph Appl 1991;11:20 – 29. West J, Fitzpatrick JM, Wang MY, et al. Comparison and evaluation of retrospective intermodality brain image registration techniques. J Comput Assist Tomogr 1997;21:554 –566. Klabbers BA, deMunck JC, Slotman BJ, et al. Matching PET and CT scans of the head and neck area: Development and method of validation, Med Phys 2002;29:2230 –2238. Noz ME, Maguire Jr GQ, Zeleznik MP, et al. Versatile functional-anatomic image fusion method for volume data sets. J Med Syst 2001;25:297–307. Ellis RJ, Sodee DB, Spirnak JP, et al. Feasibility and acute toxicities of radioimmunoguided prostate brachytherapy. Int J Radiat Oncol Biol Phys 2000;48:683– 687. Sodee DB, Conant R, Chalfant M, et al. Preliminary imaging results using In-111 labeled CYT-356 (ProstaScint) in the detection of recurrent prostate cancer. Clin Nucl Med 1996; 21:759 –767. Sodee DB, Ellis RJ, Samuels MA, et al. Prostate cancer and prostate bed SPECT imaging with ProstaScint: Semiquantitative correlation with prostatic biopsy results. Prostate 1998; 37:140 –148. Erdi YE, Wessels BW, Loew MH, et al. Threshold estimation in single emission computed tomography and planar imaging for clinical radioimmunotherapy. Cancer Res 1995;55(Suppl): 5823–5826. Pfluger T, Vollmar C, Wismüller A., et al. Quantitative comparison of automatic and interactive methods for MRI-SPECT image registration of the brain based on 3-dimensional calculation of error. J Nucl Med 2000;41:1823–1829. Studholme C, Hill DLG, Hawkes DJ. Automated three-dimensional registration of magnetic resonance and positron emission tomography brain images by multiresolution optimization of voxel similarity measures. Med Phys 1997;24:25–35. Woods RP, Mazziotta JC, Cherry SR. MRI-PET Registration with automated algorithm. J Comput Assist Tomogr 1993;17: 536 –546. Pelizzari CA, Chen GTY, Spelbring DR, Accurate threedimensional registration of CT, PET and/or MR images of the brain. J Comput Assist Tomogr 1989;13:20 –26. Meyer CR, Boes JL, Kim B, et al. Demonstration of accuracy and clinical versatility of mutual information for automatic multimodality image fusion using affine and thin plate spline warped geometric deformations. Med Image Analysis 1996/7; 3:195–206.
doi:10.1016/j.ijrobp.2004.05.034
PHYSICS CONTRIBUTION
PROCEDURE FOR UNMASKING LOCALIZATION INFORMATION FROM PROSTASCINT SCANS FOR PROSTATE RADIATION THERAPY TREATMENT PLANNING J. KEITH DEWYNGAERT, PH.D.,* MARILYN E. NOZ, PH.D,† BRUCE ELLERIN, M.D.,储 ELISSA L. KRAMER, M.D., GERALD Q. MAGUIRE, JR., PH.D.,‡ AND MICHAEL P. ZELEZNIK, PH.D.§ *Department of Radiation Oncology, New York University Medical Center, New York, NY; †Department of Radiology, New York University Medical Center, New York, NY; ‡Institution for Microelectronics and Information Technology, Royal Institute of Technology, Kista, Sweden; §Department of Radiation Oncology, University of Utah, Salt Lake City, UT; and 储RAHD Oncology Products, St. Louis, MO Purpose: To demonstrate a method to extract the meaningful biologic information from 111In-radiolabeled capromab pendetide (ProstaScint) SPECT scans for use in radiation therapy treatment planning by removing that component of the 111In SPECT images associated with normal structures. Methods and Materials: We examined 20 of more than 80 patients who underwent simultaneous 99mTc/111In SPECT scans, which were subsequently registered to the corresponding CT/MRI scans.A thresholding algorithm was used to identify 99mTc uptake associated with blood vessels and CT electron density associated with bone marrow. Corresponding voxels were removed from the 111In image set. Results: No single threshold value was found to be associated with the 99mTc uptake that corresponded to the blood vessels. Intensity values were normalized to a global maximum and, as such, were dependent upon the quantity of 99mTc pooled in the bladder. The reduced ProstaScint volume sets were segmented by use of a thresholding feature of the planning system and superimposed on the CT/MRI scans. Conclusions: ProstaScint images are now closer to becoming a biologically and therapeutically useful and accurate image set. After known sources of normal intensity are stripped away, the remaining areas that demonstrate uptake may be segmented and superimposed on the treatment-planning CT/MRI volume. © 2004 Elsevier Inc. ProstaScint, Volume fusion, Volume subtraction, Prostate cancer treatment planning, Radioimmunoguided therapy.
traditional external-beam therapy with respect to sparing of normal tissues, all techniques still suffer from nonspecificity of dose application. The target volume for both techniques is the anatomic prostate, whereas the true biologic clinical target volume is the prostate cancer, which may occupy a smaller or a larger physical volume than the anatomic prostate. Because of the direct adjacency of dose-sensitive critical structures (the bladder superiorly, the rectum posteriorly, the penile bulb anteroinferiorly, and the urethra interiorly), the use of the anatomic prostate (rather than the prostate cancer) as the basis for the clinical target volume necessarily limits the total dose that can be administered to the tumor insofar as the adjacent structures, rather than the prostate cancer, dictate the doses that are employed, even when the prostate cancer lies at a relatively safe distance
INTRODUCTION Because radiation therapy dose escalation may be associated with a higher cure rate for certain localized cancers, such as prostate cancer (1–5), and although dose escalation, in principle, is associated with an increased risk of complications to surrounding normal tissues that can also be affected by factors of technique and irradiated volume (6 – 8), interest has always been great among radiation oncologists in finding ways to confine escalated dose to tumor while sparing surrounding normal tissues. In the case of localized prostate cancer, techniques currently in use include interstitial brachytherapy (9 –11), three-dimensional (3D) conformal radiation therapy (12), and intensity-modulated external-beam radiation therapy (IMRT) (13–15). Although these techniques represent considerable improvements over
LA, October, 2002. Acknowledgment—We thank Antoinette Murphy-Walcott, CNMT and Chris J. Shettino, B.A. for fusing the images. Received Oct 22, 2003 and in revised form May 10, 2004. Accepted for publication May 12, 2004.
Reprint requests to: J. Keith DeWyngaert, Ph.D., New York University, 550 First Avenue, New York, NY 10016. Tel: (212) 2635836; Fax: (212) 263-6274; E-mail: keith.dewyngaert@ med.nyu.edu Presented at the annual meeting of the American Society for Therapeutic Radiology and Oncology (ASTRO), New Orleans, 654
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from these surrounding normal tissues when appropriate contemporary conformal techniques are used. More recent radiobiological thinking has come to favor the application of higher fractional doses to the histologic prostate cancer as the tumor burden within the prostate increases (16). Because of the well-established positive correlation between increasing total dose and acute effects on the one hand and between increasing fraction size and late effects in normal tissues on the other hand, (17) tumorspecific targeting for the purpose of either fraction-size escalation (to 3 Gy/fraction or higher)(13) or of total-dose escalation (to 81 Gy or higher) (18) needs to be performed with utmost precision to minimize acute and late effects on surrounding normal tissues. Fortunately, progress in directing dose to targeted structures with increasingly conformal treatment techniques has been paralleled by progress in defining actual tumor extent with sophisticated molecular and radiographic techniques. Molecular markers such as glycoprotein A-80 (19) and osteocalcin (20) are often found selectively in prostate cancer cells and can be used to distinguish malignant from benign histology within a single specimen. Capromab pendetide is a monoclonal antibody that adheres to prostate-specific membrane antigen (PSMA) and, when combined with 111In, the radiolabeled capromab pendetide (ProstaScint; Cytogen Corporation, Princeton, NJ), can be used to image prostate cancer (21), primarily in lymph nodes and distant soft-tissue sites (22). At the same time, specialized imaging protocols have been developed (e.g., in sonography) to select for physical characteristics (23) that may be expressed more commonly in diseased than in healthy tissue (24), in malignant than in benign tissue (25), and in cancerous than in normal prostate tissue (26). A similar concept has been applied in nuclear magnetic resonance imaging and magnetic resonance spectroscopy to distinguish prostate cancer from benign tissue (27), which has also found application in radiation treatment planning (28, 29) and treatment evaluation (30). A further refinement of this process, which aims to increase the specificity of image-based detection of tumors, is the use of fusion technology to link the image sets of two distinct imaging modalities, such as CT and MRI, for radiosurgery planning (31) and magnetic resonance spectroscopy and CT and/or ultrasound for prostate cancer (32). Since the introduction of digital imaging in radiology, images have been transformed from mere records of radiation passage through photographic emulsion to dynamic data sets whose numerical correlates can be manipulated to emphasize volumes of interest and to efface radiographic noise. One of the earliest applications of this principle was digital subtraction angiography, but the principle has subsequently been applied to magnetic resonance angiography whereby unwanted information is digitally eliminated from the data set to emphasize the volume of interest (33). As the number of molecular techniques and imaging protocols for prostate cancer detection increases and becomes more accurate, the possibility of combining techniques and protocols to in-
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crease the accuracy of prostate cancer identification for radiation treatment planning presents itself. In a preliminary experiment, a previously described and validated fusion technique (34) was used to retrospectively match (fuse) the ProstaScint SPECT scans of more than 80 patients with CT scans, MRI scans, or both. With the addition of the anatomic information, the fusion process was shown to improve the diagnostic interpretation of the SPECT data (35, 36). In this study, we examine whether the imaging information obtainable from MRI and/or CT in combination with ProstaScint, when processed by the techniques of image fusion and digital subtraction, might increase the specificity of prostate cancer identification of postprostatectomy patients with a rising prostate-specific antigen (PSA) to a degree that would allow clinicians to reliably distinguish between those patients in whom recurrent prostate cancer is confined to the prostate bed and those in whom the prostate cancer has spread outside the prostate bed. If cancer is confined to the prostate (in preprostatectomy or postprostatectomy patients), then radiation therapy in the form of external therapy, or brachytherapy, is the treatment of choice. If cancer has spread outside the prostate, then systemic therapy is generally applied. This article is an initial step toward using routine diagnostic imaging information to achieve the goal of localizing cancer for treatment planning. It follows on the work that has been done for better placement of seed implants in brachytherapy (29). METHODS AND MATERIALS Patients A group of 20 patients was selected for analysis from a larger population of patients accrued since 1998 who had fused volume data sets consisting of ProstaScint scans and either MRI (n ⫽ 9) or CT (n ⫽ 11) studies. These 20 patients were selected because they had been referred for treatment to our Radiation Oncology Department. All patients except 1 were post–radical prostatectomy. The patients had been referred for ProstaScint scans because of rising PSA levels. All patient data were acquired by use of routine protocols; no special acquisition procedures were performed. The patients ranged in age from 47 to 81. This study was a retrospective study approved by the Institutional Board of Research Associates. SPECT volumes All patients underwent planar and SPECT scans of the chest, abdomen, and pelvis 96 hours (4 days) after infusion of 185–222 MBq (5– 6 mCi) of 111In-labeled ProstaScint. On the day the ProstaScint scans were to be acquired, the patients were injected with 222 MBq (6 mCi) of 99mTc red blood cells (RBCs) so that a simultaneous blood pool scan could be acquired. The ProstaScint and RBC images were obtained concurrently and were spatially aligned as confirmed by quality control measurements. All data sets were acquired on a dual-headed gamma camera (GC7200-DI;
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Toshiba America Medical Systems, Tustin, CA) that was fitted with medium-energy collimators and used 20% energy windows centered on 173 keV and 247 keV to acquire the 111In images and used a 15% energy window centered on 140 keV to acquire the 99mTc blood pool images. Volume data sets were acquired into 128 ⫻ 128 ⫻ 2 byte matrices with a total of 90 projections and a time per projection of 50 seconds and were reconstructed by application of a commercially supplied algorithm that consisted of a Butterworth prefilter (eighth order; cutoff frequency ⫽ 0.18 cycles/cm) and ramp backprojection, which resulted in a cubic voxel with dimensions of 4.3 mm on a side. The gamma camera full width at half maximum (FWHM) is 9.3 mm. MRI Volumes All the MRI volumes were acquired on a 1.5 T scanner (Siemens Magneton Symphony or Vision System, Iselin, NJ) by implementation of the standard pelvic anatomic protocol, which resulted in several different T1-weighted and T2-weighted volume data sets. The volume data sets used for the fusion were reconstructed into matrix sizes of 512 ⫻ 512 ⫻x 2 bytes with an x-y pixel size that ranged from 0.49 to 0.53 mm and a total spacing between slices that ranged from 4.4 to 5.3 mm with one scan at 8.8 mm. The axial T2 Spin Echo sequence was used for the fusion. CT Imaging All the CT volumes were acquired (Highlight Advantage or High Resolution, General Electric Corporation, Milwaukee, WI) after the administration of oral and/or i.v. contrast (Conray 43; Mallinkrodt, St. Louis, MO). The resulting 50 to 60 transaxial sections were each acquired into a 512 ⫻ 512 ⫻ 2 byte matrix. All the volume sets had a slice thickness that ranged from 5.0 to 7.0 mm with one image set at 8.8 mm. The x-y pixel size varied between 0.64 and 0.94 mm. Data handling All volume data sets (CT/MRI/SPECT) were transferred via the hospital ethernet (TCP/IP) network to a common computer system (Sun SPARC Ultra10; Sun Microsystems Inc., Mountain View, CA) and converted to a common, standard image format (Interfile/AAPM)(37). Original data were transferred in DICOM 3 (CT/MRI) or NEMA 1 (SPECT) format and were not altered by the conversion to Interfile/AAPM format. Software modules were developed to convert the processed SPECT scans, which had been aligned with the diagnostic CT/MRI scan to DICOM 3 format, so that they could be imported by the treatmentplanning system (Eclipse; Varian Medical Systems, Palo Alto, CA). These warped SPECT studies were disguised as MRI scans because there was no SPECT modality assignment available in the planning system. Any modality specification other than “MR” defaulted in our treatment-planning system to a CT, and this assignment restricted the intensity levels to expected Hounsfield numbers. If we des-
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ignated that the SPECT volumes were of modality “MR,” the treatment-planning system accepted them without perversion of the intensity values.
Volume fusion Image fusion has previously been attempted for ProstaScint SPECT scans by application of a vascular outlining method, in which large vessels of the abdomen were outlined by computer-generated wire rings on SPECT 99mTc blood-pool images and CT images. The vascular outlines on the two-image sets were then aligned manually, and the resulting spatial relationships served as the basis for the registration (38). In our study, fusion of ProstaScint SPECT and CT/MRI scans were performed by a computer program, codeveloped with RAHD Oncology Products, that performs landmark-based fusion of 2 volumes by application of a polynomial warping algorithm (34). The registration algorithm incorporated in this tool has previously been extensively validated (39, 40) and can be used to produce an affine (the original relationship between the structures involved, that is, the straightness of lines, parallelism, and the ratio between the length of 2 segments of the same line is preserved) or nonaffine transformation, in this case, warping (nonaffine transformation has more degrees of freedom, which allows line lengths and angles to change, allows straight lines to become curved, and does not preserve parallelism). The technical approach to image fusion in the thorax, abdomen, and pelvis is more complicated than in the brain because different shapes, sizes, and angles can occur; bowel can distend and deflate; and respiratory motion is always present. Therefore, the ability to stretch, warp, or deform images is important to achieving a good fusion. Even when images for fusion are acquired prospectively, differences in positioning may be reduced. However they are not completely eliminated (41). Arbitrarily chosen slices (with optionally superimposed isolines of the user’s choosing) are presented together in all 3 planes (axial, coronal, and sagittal) for both the anatomic and functional volume sets (6 views) or as larger views (2) in 1 user-selected plane (42). Corresponding point pairs (landmarks) were chosen on concurrently viewed slices that display the same physiologic point or structure. Landmarks could be selected in multiple planes simultaneously. When a landmark was chosen, the corresponding volume-element in the image, the voxel, was marked, a sequential number generated, and all 3 planes triangulated to this point. All landmarks were recorded at the respective 3D point in distance units (mm in this case). The 3D paired landmarks were used to generate the transformed volume. The eigenvalues of the matrix of coefficients are generated from the 3D paired landmarks by employing a weighted least-squares linear regression followed by a Gauss-Jordan matrix inversion. This procedure limited the effects of mismatched landmarks and provided an easy way to generate transformation coefficients for arbitrary volume data sets. Finally, polynomial equations
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Fig. 1. 99mTc images were registered to the computed tomography (CT) images with landmarks indicated on orthogonal images. 99mTc intensities above the threshold limit were overlaid on the CT images and varied to determine the appropriate threshold value. The corresponding isoline values are shown as an overlay on the 99mTc images. (Top) SPECT with isolines delineating regions of intensity above the threshold. (Bottom) CT with superimposed regions of SPECT intensity above threshold. (A) Coronal view; (B) axial view.
formed from the eigenvalues, together with resampling, determined the voxel values in the warped volume that brought it into correspondence with the reference volume; that is, each original data point was moved to the transformed position. The transformation could be performed by use of only the first-order terms (12 parameters in all) or by use of the second-order terms (30 parameters in all) from the polynomial equations (34, 42). A completely manual (translate, rotate, and scale only) affine transformation was also available to do a complete fusion or to “tweak” the above transformation to bring the 2 volumes into better alignment if required. In this experiment, the SPECT volume was always transformed to correspond to the anatomic volume; that is, the SPECT data sets were warped (preserving total counts) to fit the MRI or CT. Hence the original anatomic data sets that would be used in the treatment-planning process were not altered. Magnetic resonance volumes may contain geometrical distortions. However, the purpose of registration between SPECT and MR is to assign anatomic meaning to the SPECT information, not geometrical meaning. For purposes of treatment planning, a CT scan is used as a geometrically accurate reference image.
Thresholding Once the new (warped) volume had been produced from the original, untransformed volume data set, it can be resliced and evaluated side-by-side or merged with the reference slices. In side-by-side mode, isointensity lines (isolines) from the warped volume could be superimposed on the reference slices. The SPECT radionuclidic uptake was normalized to that studies’ maximum counts. As the 99m Tc was tagged to red blood cells, the 99mTc images represented blood vessels and normal structure reservoirs. The process was to select through visual inspection the intensity value (isoline) that most closely matched the blood vessels seen on the reference (CT/MRI) image set. As the isolines were varied, the reference image set was examined by paging through the reference set of axial, coronal, and sagittal images and observing the overlap of the 99mTc isolines. An alternate presentation of the same process, shown in Fig. 1, could be achieved by blending the 2 image sets, CT and 99mTc, in an overlay mode such that only those voxels of the 99mTc image whose intensities exceeded the selected isoline (threshold) value were viewable. The advantage of this representation was that it maintained the intensity variation of the SPECT image in the overlay. The images could
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Fig. 2. The 99mTc threshold values representing the best fit of the computed tomography or magnetic resonance imaging blood vessels are shown for the 20 cases studied.
be viewed in the 3 orthogonal planes simultaneously with varied levels of transparency. After the isoline or threshold value had been established, a new reduced 99mTc image set was created that contained only those voxels whose counts exceeded the threshold value. Because the SPECT images were acquired as a simultaneous dual-SPECT image set, the unwarped 99mTc is self-registered with the original ProstaScint 111In volume set. Therefore, the new thresholded 99mTc image set, which consisted of the voxels identified as representative of bloodpooling structures could be directly subtracted from the 111 In ProstaScint image set. In practice, the voxels were not subtracted, but rather assigned an intensity value of zero. Boolean Subtraction of bone marrow ProstaScint uptake associated with the bone marrow was removed through the introduction of Boolean operations available within the treatment-planning system. Contours were generated from the ProstaScint pixel values by application of the planning system’s threshold segmentation algorithm. The same warping transformation described earlier for the 99mTc images was utilized to register the ProstaScint images to the reference image set, CT or MRI. As before, the SPECT images were superimposed on the reference images to allow inspection of threshold levels for anatomic relevance. A threshold value was chosen that excluded the background 111In counts in the ProstaScint SPECT scans. The same threshold segmentation algorithm was also applied to the CT data set to define the pelvic bony structures.
Areas of overlap between the CT-defined bone and the 111In scan were excluded as a normal pooling site for the ProstaScint. This action was accomplished by use of a Boolean operator that subtracted the bone volume from the ProstaScint segmented volume. RESULTS The threshold values chosen for the 20 patients are shown in Fig. 2. The counts per pixel were normalized to the maximum counts for the volume set. For the 99mTc data sets, the highest counts were associated with the bladder. The threshold values range from 9% to 54% of the maximum volume intensity. All 99mTc studies were successfully thresholded and subtracted from the ProstaScint study. Figure 3 depicts the thresholded 99mTc study, overlay of the isoline value onto the 111In image, and the subsequent 111In after the overlapping 99mTc image voxels were zeroed. The SPECT volume studies, which included the subtracted ProstaScint study, were fused with the reference image set, and the resulting SPECT volumes were saved to disk. These warped SPECT studies were reformatted into DICOM and imported into the treatment-planning system. The SPECT scans were successfully segmented using the treatment-planning software’s thresholding capabilities. This thresholding algorithm was applied to segment bone on CT and sites of 111In activity on the ProstaScint images, which created 3D volume sets of bone and ProstaScint.
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Fig. 3. The process of removing normal structures from the 111In volume set starts with identifying the threshold value for 99mTc images as shown in Fig. 1. A new thresholded 99mTc image set was created (A) and superimposed onto the 111 In images (B) and subsequently these voxels were set to zero intensity in the 111In images (C). This last set was sent to the treatment-planning system for evaluation.
Figure 4 shows the contours of the pelvic bone, the 99mTc blood uptake, and the remaining 111In uptake superimposed onto either a CT image (Fig. 4A) or a ProstaScint image (Fig. 4B). The final step of removing potential background uptake from normal tissues was to subtract the segmented bone from the segmented ProstaScint. All the volume sets were warped before exporting to the treatment-planning system, so no additional registration was required. DISCUSSION Molecular imaging is increasingly being considered as a method for target localization in radioimmunoguided radiation therapy (43). ProstaScint is an example of a marker developed for use in targeting prostate cancer through specificity for PSMA (32, 44, 45). However, the ProstaScint SPECT images are difficult to interpret because the tagged antibody or disassociated 111In collects in structures such as the vascular structures, liver, kidneys, bone, and bone marrow (see Fig. 3B). Our goal was to establish a clinically applicable procedure for taking these seemingly unintelli-
gible images, by radiation oncology standards, and transforming them such that they can be a useful component in the treatment-planning process. This transformation involved filtering out the background of information that obscured the pathologic findings, warping the image set to match a reference set, importing the images into our commercial treatment-planning system, and segmenting the SPECT images. We have not yet evaluated how our method would help the radiation oncologist with the actual treatment plan. We are refining our subtraction technique, and, after that, we will conduct a retrospective study to assess what, if any, impact our method would have on the actual treatment plans. The subtraction process initiates with a determination of the threshold value that corresponds to the blood vessels. As such, it builds upon the warping algorithm and the selfregistration of the dual-SPECT imaging. The threshold value that most closely resembles the underlying biology is dependent upon both scanning and patient parameters, such as the total scan volume, the image contrast and spatial
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resolution, and bladder filling (46). Phantom tests designed to determine threshold values for use in SPECT imaging have resulted in a wide range of values from 40% for good-contrast images to 70% for low-contrast images (46). We have, in general, found that much lower thresholds have applied when considering the abdomen and pelvis as the region of interest. No single formula can be applied to determine the appropriate threshold when considering such a large volume that contains many sites of radionuclidic uptake. For example, in defining threshold values that represent vascular structures for 99mTc, our values were dominated by the bladder intensity and ranged from 9% to 54%. This large variation in maximum bladder intensity occurs for two reasons: (1) physiologic parameters associated with the patient, such as whether or not the patient is taking diuretics, and (2) the ability of the patient to void completely and how long before the acquisition of the pelvic SPECT scan the patient has attempted to empty his bladder. This problem can perhaps be overcome by the use of a Foley catheter. We have not applied this method because of the discomfort and inconvenience it would cause to the patient. Instead, we have pursued a mathematical solution that suppresses the bladder activity by use of an averaging algorithm within an ellipsoid volume encompassing the bladder. Outside of this ellipsoidal volume, the blood-vessel activity as determined by the 99mTc scans is subtracted from the 111In after normalization of the data sets on the basis of peak activity within the descending aorta. The reason for this attention to suppression of the bladder uptake may be seen from Fig. 4, which illustrates the capabilites of this approach to localize suspicious uptake in regions in close proximity to the bladder. Figure 1 demonstrates how overwhelming this uptake within the bladder may be compared with the other sites within the SPECT image. Care must be taken not to remove activity from the 111In scan that might be indicative of prostate disease or prostate bed recurrence. A patient with an intact prostate was chosen for Fig. 4 to help illustrate the concepts of localizing disease by remov-
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ing sources of activity from the bone marrow and blood vessels. Enough significant uptake occurs within the prostate to segment on the treatment-planning system. The process was the same for postprostatectomy patients. The registration/fusion tool used here requires between 10 to 20 minutes for the registration to be completed; that is, volumes reviewed, landmarks chosen, warp performed, and result accepted. The actual warping transformation itself takes 10 to 50 seconds and depends only on the number of voxels to be transformed. Additionally, this tool is user friendly and can be used by trained individuals who have a fair knowledge of anatomy (e.g., a radiologic technologist, medical student, or physician). This tool has proved to be suitable for other routine clinical use, particularly in terms of speed and user friendliness. A convenient feature of this tool is that by allowing the selection of landmarks in multiple planes, the user can view the physiologically/anatomically corresponding points from different points of view, which often strengthens the positive identification of corresponding features. Furthermore, the incorporation of isolines into the tool adds additional information and can be very useful on the actual display. The ability to merge both the 2D slices and 3D volume sets to visually demonstrate the degree of overlap is a very useful complement to the isoline display. Although the method is interactive, in that point pairs must be chosen, a recent study has shown that the chief objections to interactive versus automated registration (specifically interobserver and intraobserver differences) are not as severe as they were once thought to be (47). Methods that are claimed to be fully automatic generally require user interaction either before or during the registration process (48 –50) or require a tremendous amount of prospective preparation, which, thus, excludes retrospective correlation (41), especially when the region to be matched is not the brain (51). Treatment-planning systems are not designed to detect subtle specificity differences of SPECT uptake above the
Fig. 4. Contours are presented for the computed tomography (CT)-derived pelvic bone anatomy (green), the 99mTc blood vessels (yellow) and the unaltered 111In ProstaScint uptake (cyan). The 3 contours are overlaid on a (A) CT image and on the (B) corresponding ProstaScint image. Areas of overlap are shown representing the blood vessels as well as a ProstaScint-only uptake located in the prostate as seen on the CT image.
Procedure for unmasking ProstaScint scans
background and noise. They are designed to work with a defined set of well-described volumes. Nuclear medicine systems are designed to guide the user in defining areas of enhanced radionuclidic activity but are not designed, in general, for the segmentation of volumes, as might be necessary in radiation therapy treatment planning. We extended the ability of our Nuclear medicine– based system to allow for the subtraction of areas contained within a given threshold value (isoline) from an accompanying registered image. If the CT bone-density threshold values were applied to the 111In scans in our Nuclear medicine system, the area where the bones were (even though not usually visible unless some disassociated 111In is present in the scan) would be subtracted but not the areas enclosed by the bones (i.e., the bone marrow), which was visible on the SPECT scan. Outlining the bone marrow only on either the CT or the SPECT was very difficult. Because the treatment-planning system segments everything within the contours by outlining the bone by use of a thresholding technique, the bone marrow was included as an internal component of the contour. This ability to perform Boolean operations between different segmented volume sets is a powerful tool that allows molecular imaging to be integrated more fully into
● J. K. DEWYNGAERT et al.
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the treatment-planning process by removing uptake associated with normal structures in an automated approach. In conclusion, the molecular images when fused with the CT/MRI and integrated into the treatment-planning workstation, may identify areas either within the prostate or external to the prostate that are suggestive of disease. We have demonstrated the proof of concept of incorporating the ProstaScint data for use in external-beam radiation therapy treatment planning in a meaningful way. Nuclear medicine data may be integrated into the radiation oncology treatment-planning environment through the application of thresholding algorithms that segment the SPECT images for structure. The background counts associated with normal structures may be removed from the data set, which allows the oncologist to see through the fog associated with the ProstaScint image set by focusing on uptake not associated with normal structures. In this regard, Boolean operators are powerful tools to help decode the SPECT image pattern. Inclusion of this additional information may help redefine the target for both conventional 3D and IMRT treatment planning, as has been demonstrated for brachytherapy treatment of prostatic disease (29, 42, 43).
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